James Raybould on Being AI-Forward
Second post in series interviewing people on how they use AI
James Raybould came recommended to me by multiple people when I posted about asking for AI savants, and he did not disappoint. Thank you Maisy Samuelson for the introduction!
There are so many tidbits he shared on his mindset which he describes as “AI-Forward”. I am going to include a bunch of quotes on his thinking (slightly edited for brevity). And then he shared a few fun examples on how he uses AI day-to-day.
James’s Mindset and Approach
"I just assume anything I'm doing, like almost every decision I'm making is better with AI. At this point, it's been built into almost every single thing. If I'm trying to name my team. I'm trying to write my LinkedIn post. I'm trying to write a strategy. I'm trying to think of anything I do. I go to Claude, my favorite, or my default tab is Perplexity."
"I'd say if you look at my podcast time, it's probably gone from three to five hours a week to probably one hour a week because for the other three to four hours advanced voice mode has taken over.... I think that my biggest realization is again, AI knows more about everything than we know about anything. And so therefore, why wouldn't I be curious? And so therefore if you have a choice of talking to the most knowledgeable person who has ever lived, why wouldn't I do that on just about everything?"
"I think that you and I grew up with this idea that 'Effort equals output'. I'm like, oh cool, you must have put so much time into this Sarah. Oh, what an amazing dossier you wrote. What an amazing briefing. Now we're moving to this world where the connection between effort and output is completely disappearing." To this point, James mentioned the example Ethan Mollick gives in his book Co-Intelligence on writing recommendation letters for his students. It used to be a signal when a Professor would take the time to write a recommendation letter for a student, because that effort would create scarcity. The professor isn't going to say yes to everyone who asks. What happens to the value of a recommendation letter when AI can do it in three minutes?
"I keep on reminding myself, like Ethan Mollick says, AI is the worst it will ever be today."
Use Case Examples
The three big themes for James are about 1) brainstorming and writing partner, 2) synthesizing information for him, and 3) scouring the web and other sources for information that is relevant to him (vs him having to go out and get it himself).
Three examples he gives:
Brainstorming and writing partner with Claude:
"Anything, LinkedIn posts, LinkedIn long form articles, and internal stuff, like documents internally, phrasing's a lot. I find there's a lot of like, what should I call this team? What should I call this? Is there a really pithy phrase? The key there is that you ask for 25 ideas. And then say, I only like two and four. Give me 25 more. And you say, I only like one, two, and three. You just keep going.”
"Each average idea for any human is probably better than each average idea for an AI. But the AI can give you 200 of them in 10 seconds and the human can give you like 13 in like two hours. So Claude can give you 200 taglines, 150 are absolute garbage. But 50 are pretty good and 10 are magic."
He often prompts Claude after it's written something, "Let's remove the jargon and make it more like me."
Bringing information to you.
James has started experimenting with OpenAI’s Operator. "It's not that each individual task is amazing. My mind was blown because I'm watching TV with with my kids, and in parallel, I'm preparing a dossier on you, and I'm finding some candidates for a search, and I'm researching our dishwasher. The average person doesn't have an EA or a Chief of Staff to give work to, but now they do."
To show how simple it was for James to get value out of Operator, here is the prompt he used:
"I'm preparing to meet Sarah Tavel from Benchmark on Monday. Can you please create a full Google document dossier style that tells me as much as you can about her. Please start with broader information and then narrow down to anything that she's written about AI on LinkedIn, X, her blog, or other public sources in the past 12 months."
You can watch Operator in action here. And the output here (with some formatting from James). Definitely not perfect, but as James reminds us, “worst it will ever be.”
Daily email newsletter synthesizer, built with Relay
One of the core use cases James uses AI for is synthesizing content. A great example of this is for newsletters. Like all of us, he is subscribed to a lot of email newsletters (make this one more!), and often doesn’t read everything or know what to focus on. So he built a tool using Relay that ingests the newsletters in his Gmail, and then synthesizes the content so he gets an easy-to-glance summary every morning, and he can then dive in on the posts he’s curious about.
This use case is a classic example to me of something I’m noticing the “AI Savants” I’ve spoken to all have in common — they are willing to do the extra upfront effort to get the downstream benefits.
James kindly put together 10 slides to show how he does it here: Getting a Daily Digest of the So Whats from a Series of Newsletters using relay.app.
Here’s an example of what the output is. Check it out!!
I'd be interested in hearing more about *results* from using AI as described above. Nobody should have any trouble believing in the dramatically increased efficiency and its effect on overall operations. But has James, or anyone else, noticed a similar increase in results? Even anecdotally, are the "magic" headlines performing better? Any thoughts as to why, if so? If you're using AI as a "centaur", using your experience to improve on the raw output, what do you think you've improved most, or why is yours better?
Good use cases. Actually trying to build something that allows Claude to go into a specific gmail folder of mine to analyze the content (in my case, marketing sequences) so I can see what is and is not effective.